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Related Experiment Video

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Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

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Published on: December 15, 2023

Latent Network Construction for Univariate Time Series Based on Variational Auto-Encode.

Jiancheng Sun¹, Zhinan Wu^2,3, Si Chen¹

¹School of Software and Internet of Things Engineering, Jiangxi University of Finance and Economics, Nanchang 330013, China.

Entropy (Basel, Switzerland)

|August 27, 2021

Summary

This study introduces a novel method for time series analysis by converting them into complex networks using Variational Auto-Encoder (VAE). This approach effectively retains time series information and creates a new data structure for advanced analysis.

Keywords:

complex network latent space statistical manifold time series

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Area of Science:

Data Science
Network Science
Time Series Analysis

Background:

Time series analysis is crucial for information processing.
Converting time series to complex networks offers new analytical perspectives.
Existing methods may not fully capture the underlying structure of univariate time series.

Purpose of the Study:

To explore the construction of latent networks for univariate time series.
To utilize Variational Auto-Encoder (VAE) for latent network generation.
To develop a novel data structure for enhanced time series analysis.

Main Methods:

Trained a Variational Auto-Encoder (VAE) to generate latent probability distributions.
Decomposed multivariate Gaussian distributions into univariate Gaussian distributions.
Measured distances between univariate Gaussian distributions on a statistical manifold for network construction.

Main Results:

Successfully constructed latent networks from univariate time series.
Demonstrated that the latent network effectively retains original time series information.
The proposed latent network serves as a valuable new data structure.

Conclusions:

The VAE-based latent network construction is a viable method for time series analysis.
Latent networks offer a promising new data representation for downstream tasks.
This approach enhances the understanding and analysis of univariate time series data.